Investigating the Effects of Social Interactive Behaviours of a Robot on People’s Trust During a Navigation Task

Rossi, Alessandra; García, Fernando; Maya, Arturo Cruz; Dautenhahn, Kerstin; Koay, Kheng Lee; Walters, Michael L.; Pandey, Amit Kumar

doi:10.1007/978-3-030-23807-0_29

Cited by 18 publications

(9 citation statements)

References 33 publications

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“…Just as how much a person trusts a robot, it is important how the social system of a workplace is and how it is perceived by the workers involved. How do they see their roles, what other people and social roles are involved and what is the robot to them: Is it perceived as a teammate, a tool or the "robot boss" (for research on this, see e.g., Rossi et al, 2019)? Future research should focus on the implications such perceptions of the robot and associated self-perceptions can have on how tasks are allocated, which allocation processes are preferred under which conditions and how task allocation can contribute to a configuration of the social system that is desirable (e.g., in satisfying the need for relatedness, see Ryan and Deci, 2000).…”

Section: Implications For Researchmentioning

confidence: 99%

Psychological Effects of the Allocation Process in Human–Robot Interaction – A Model for Research on ad hoc Task Allocation

2020

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Task allocation is immensely important when it comes to designing human-robot interaction (HRI), but although it is the shaping part of the interaction, it is merely regarded as a process with its own effects on human thinking and behavior. This study aims at linking research from different fields like psychological theory, HRI and allocation optimization to create a new process model of ad hoc task allocation in human-robot interaction. It addresses the process characteristics and psychological outcomes of a real-time allocation process that integrates the worker. To achieve this, we structured the process into steps and identified relevant psychological constructs associated with them. The model is a first step toward ergonomic research on the selforganized allocation of tasks in HRI, but may also be an inspiration for practitioners designing HRI systems. To create successful work in HRI, designing the technology is an important foundation, but a participative, thought-out process for allotting tasks could be the key to adequate autonomy, work satisfaction and successful cooperation.

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Section: Implications For Researchmentioning

confidence: 99%

Psychological Effects of the Allocation Process in Human–Robot Interaction – A Model for Research on ad hoc Task Allocation

2020

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“…Autonomous robots can be viewed as unpredictable, resulting in feelings of unease and distrust [18]. Resulting from this lack of predictability are conflicts in trajectory planning (e.g., collisions) which are a common issue in HRI, as previous studies with human-robot co-existence have shown [45,46]. A long-known important factor for a safe, acceptable, and trustworthy HRI is the transparency of an autonomous system concerning its plans and actions [47][48][49][50][51][52].…”

Section: Transparency Of Robot's Actionsmentioning

confidence: 99%

Findings From A Qualitative Field Study with An Autonomous Robot in Public: Exploration of User Reactions and Conflicts

Babel¹,

Kraus²,

Baumann³

2022

Int J of Soc Robotics

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Soon service robots will be employed in public spaces with frequent human-robot interaction (HRI). To achieve a safe, trustworthy and acceptable HRI, service robots need to be equipped with interaction strategies suitable for the robot, user, and context. To gain realistic insights into the initial user reactions and challenges that arise when a mechanoid, autonomous service robot in public is applied, a field study with three data sources was conducted. In a first step, lay users’ intuitive reactions to a cleaning robot at a train station were observed ($$N = 344$$ N = 344 ). Second, passersby’s preferences for HRI interaction strategies were explored in interviews ($$n = 54$$ n = 54 ). As a third step, trust and acceptance of the robot were assessed with questionnaires ($$n = 32$$ n = 32 ). Identified challenges were social robot navigation in crowded places also applicable to vulnerable passersby, inclusive communication modalities, information of staff and public about the service robot application and the need for conflict resolution strategies to avoid an inefficient robot (e.g., testing behavior, path is blocked). This study provides insights into naive HRI in public and illustrates challenges, provides recommendations supported by literature and highlights aspects for future research to inspire a research agenda in the field of public HRI.

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“…Currently, this robot is able to prepare smoothies and cocktails. The choice to adopt an anthropomorphic structure for the robot comes from the evidence that people are social entities, and they are more comfortable while interacting with agents that can show social behaviours [18].…”

Section: A Brillo Bartender Robotmentioning

confidence: 99%

A ROS Architecture for Personalised HRI with a Bartender Social Robot

Rossi¹,

Maro²,

Origlia³

et al. 2022

Preprint

Self Cite

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BRILLO (Bartending Robot for Interactive Long-Lasting Operations) project has the overall goal of creating an autonomous robotic bartender that can interact with customers while accomplishing its bartending tasks. In such a scenario, people's novelty effect connected to the use of an attractive technology is destined to wear off and, consequently, it negatively affects the success of the service robotics application. For this reason, providing personalised natural interaction while accessing its services is of paramount importance for increasing users' engagement and, consequently, their loyalty. In this paper, we present the developed three-layers ROS architecture integrating a perception layer managing the processing of different social signals, a decision-making layer for handling multi-party interactions, and an execution layer controlling the behaviour of a complex robot composed of arms and a face. Finally, user modelling through a beliefs layer allows for personalized interaction.

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Investigating the Effects of Social Interactive Behaviours of a Robot on People’s Trust During a Navigation Task

Cited by 18 publications

References 33 publications

Psychological Effects of the Allocation Process in Human–Robot Interaction – A Model for Research on ad hoc Task Allocation

Psychological Effects of the Allocation Process in Human–Robot Interaction – A Model for Research on ad hoc Task Allocation

Findings From A Qualitative Field Study with An Autonomous Robot in Public: Exploration of User Reactions and Conflicts

A ROS Architecture for Personalised HRI with a Bartender Social Robot

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